Interpretive Summary: Practical applications of climate forecasts require simulation of agricultural impacts and evaluate profitability, risk, and decision options. However, availability of simulation software does not guarantee adoption and use of forecast and production predictions by the agricultural end-user. The broader adoption issue hinges on effective dissemination and communication of agriculture-specific decision information and its integration into the end-user's decision process. To date, agricultural applications have mostly been conducted by research agencies as demonstration projects, and existing software and prediction products have not been broadly adopted by the agricultural end-user. In this short conceptual review of the issues, adoption impediments were discussed, existing dissemination models for climate forecasts and prediction products were reviewed, and an approach to promote adoption of prediction products and decision information was proposed. In the proposed approach, resource intensive components of climate forecasting and impact prediction are developed by government agencies, and a consulting service provides site- and problem-specific interpretation of the prediction products and end-user decision information. Limiting the scope and cost of personalized consulting to site-specific interpretation and decision support makes the service accessible to small agricultural users. The adoption of forecast and prediction products by the agricultural end-user could increase productivity, enhance profitability and reduce economic risk for agricultural enterprises.

Technical Abstract:
A wealth of climate forecast information and related prediction products are available, but impediments to adoption of these products by ranchers and farmers in the Unites States remain to be addressed. Impediments for agricultural applications include limited forecast skill; inappropriate forecast scale for site-specific applications; difficulties in interpretation of probabilistic forecasts by farmers and integration into agricultural decision systems; uncertainty about the value and impact of forecast information in multi-variable decision system; and generally low frequency of relevant forecasts. Various research institutions have conducted case-studies of climate impacts on agricultural production systems, particularly impacts of historical ENSO signals in the southeastern United States. A number of studies also addressed risk and economic values of seasonal climate forecasts, and others bridged the gap between current forecasting software and products and agricultural applications. Yet, little attention has been given to how these software and prediction-products could be operationally implemented for agricultural applications and delivered to the agricultural community (a pre-requisite for adoption). Two existing approaches, the top-down and the participatory, end-to-end approach for development and delivery of prediction products are reviewed. A hybrid approach is proposed that utilizes the top-down approach for climate forecast delivery and a participatory approach for development and delivery of farm-specific prediction information for the agricultural end-user. Suitability of such prediction products for agricultural applications and constraints to successful adoption are also discussed.